OpenRaiser/Intern-Atlas huggingface dataset:
Near Real-time Hippocampus Segmentation Using Patch-based Canonical Neural Network
Venue: arXiv cs.CV 2018
Year: 2018

Over the past decades, state-of-the-art medical image segmentation has heavily rested on signal processing paradigms, most notably registration-based label propagation and pair-wise patch comparison, which are generally slow despite a high segmentation accuracy. In recent years, deep learning has revolutionalized computer vision with many practices outperforming prior art, in particular the convolutional neural network (CNN) studies on image classification. Deep CNN has also started being applied to medical image segmentation lately, but generally involves long training and demanding memory requirements, achieving limited success. We propose a patch-based deep learning framework based on a revisit to the classic neural network model with substantial modernization, including the use of Rectified Linear Unit (ReLU) activation, dropout layers, 2.5D tri-planar patch multi-pathway settings. In a test application to hippocampus segmentation using 100 brain MR images from the ADNI database, our approach significantly outperformed prior art in terms of both segmentation accuracy and speed: scoring a median Dice score up to 90.98% on a near real-time performance (<1s).
The Temporary Exchange Problem
Venue: arXiv cs.GT 2018
Year: 2018

We formalize an allocation model under ordinal preferences that is more general than the well-studied Shapley-Scarf housing market. In our model, the agents do not just care which house or resource they get but also care about who gets their own resource. This assumption is especially important when considering temporary exchanges in which each resource is eventually returned to the owner. We show that several positive axiomatic and computational results that hold for housing markets do not extend to the more general setting. We then identify natural restrictions on the preferences of agents for which several positive results do hold. One of our central results is a general class of algorithms that return any allocation that is individually rational and Pareto optimal with respect to the responsive set extension.
Magnitude Bounded Matrix Factorisation for Recommender Systems
Venue: arXiv cs.LG 2018
Year: 2018

Low rank matrix factorisation is often used in recommender systems as a way of extracting latent features. When dealing with large and sparse datasets, traditional recommendation algorithms face the problem of acquiring large, unrestrained, fluctuating values over predictions especially for users/items with very few corresponding observations. Although the problem has been somewhat solved by imposing bounding constraints over its objectives, and/or over all entries to be within a fixed range, in terms of gaining better recommendations, these approaches have two major shortcomings that we aim to mitigate in this work: one is they can only deal with one pair of fixed bounds for all entries, and the other one is they are very time-consuming when applied on large scale recommender systems. In this paper, we propose a novel algorithm named Magnitude Bounded Matrix Factorisation (MBMF), which allows different bounds for individual users/items and performs very fast on large scale datasets. The key idea of our algorithm is to construct a model by constraining the magnitudes of each individual user/item feature vector. We achieve this by converting from the Cartesian to Spherical coordinate system with radii set as the corresponding magnitudes, which allows the above constrained optimisation problem to become an unconstrained one. The Stochastic Gradient Descent (SGD) method is then applied to solve the unconstrained task efficiently. Experiments on synthetic and real datasets demonstrate that in most cases the proposed MBMF is superior over all existing algorithms in terms of accuracy and time complexity.
Extended finite automata and decision problems for matrix semigroups
Venue: arXiv cs.FL 2018
Year: 2018

We make a connection between the subgroup membership and identity problems for matrix groups and extended finite automata. We provide an alternative proof for the decidability of the subgroup membership problem for $ 2 \times 2 $ integer matrices. We show that the emptiness problem for extended finite automata over $ 4 \times 4 $ integer matrix semigroups is undecidable. We prove that the decidability of the universe problem for extended finite automata is a sufficient condition for the decidability of the subgroup membership and identity problems.
Latency-Energy Tradeoff based on Channel Scheduling and Repetitions in NB-IoT Systems
Venue: arXiv cs.IT 2018
Year: 2018

Narrowband IoT (NB-IoT) is the latest IoT connectivity solution presented by the 3GPP. NB-IoT introduces coverage classes and introduces a significant link budget improvement by allowing repeated transmissions by nodes that experience high path loss. However, those repetitions necessarily increase the energy consumption and the latency in the whole NB-IoT system. The extent to which the whole system is affected depends on the scheduling of the uplink and downlink channels. We address this question, not treated previously, by developing a tractable model of NB-IoT access protocol operation, comprising message exchanges in random-access, control, and data channels, both in the uplink and downlink. The model is then used to analyze the impact of channel scheduling as well as the interaction of coexisting coverage classes, through derivation of the expected latency and battery lifetime for each coverage class. These results are subsequently employed in investigation of latency-energy tradeoff in NB-IoT channel scheduling as well as determining the optimized operation points. Simulations results show validity of the analysis and confirm that there is a significant impact of channel scheduling on latency and lifetime performance of NBIoT devices.
Consensus Needs Broadcast in Noiseless Models but can be Exponentially Easier in the Presence of Noise
Venue: arXiv cs.DC 2018
Year: 2018

Consensus and Broadcast are two fundamental problems in distributed computing, whose solutions have several applications. Intuitively, Consensus should be no harder than Broadcast, and this can be rigorously established in several models. Can Consensus be easier than Broadcast? In models that allow noiseless communication, we prove a reduction of (a suitable variant of) Broadcast to binary Consensus, that preserves the communication model and all complexity parameters such as randomness, number of rounds, communication per round, etc., while there is a loss in the success probability of the protocol. Using this reduction, we get, among other applications, the first logarithmic lower bound on the number of rounds needed to achieve Consensus in the uniform GOSSIP model on the complete graph. The lower bound is tight and, in this model, Consensus and Broadcast are equivalent. We then turn to distributed models with noisy communication channels that have been studied in the context of some bio-inspired systems. In such models, only one noisy bit is exchanged when a communication channel is established between two nodes, and so one cannot easily simulate a noiseless protocol by using error-correcting codes. An $Ω(ε^{-2} n)$ lower bound on the number of rounds needed for Broadcast is proved by Boczkowski et al. [PLOS Comp. Bio. 2018] in one such model (noisy uniform PULL, where $ε$ is a parameter that measures the amount of noise). In such model, we prove a new $Θ(ε^{-2} n \log n)$ bound for Broadcast and a $Θ(ε^{-2} \log n)$ bound for binary Consensus, thus establishing an exponential gap between the number of rounds necessary for Consensus versus Broadcast.
Three-dimensional Stable Matching with Cyclic Preferences
Venue: arXiv cs.GT 2018
Year: 2018

We consider the three-dimensional stable matching problem with cyclic preferences, a problem originally proposed by Knuth. Despite extensive study of the problem by experts from different areas, the question of whether every instance of this problem admits a stable matching remains unanswered. In 2004, Boros, Gurvich, Jaslar and Krasner showed that a stable matching always exists when the number of agents in each of the groups is three. In 2006, Eriksson, Sjöstrand and Strimling showed that a stable matching exists also when the number of agents in each group is four. In this paper, we demonstrate that a stable matching exists when each group has five agents. Furthermore, we show that there are at least two distinct stable matchings in that setting.
Improving the smoothed complexity of FLIP for max cut problems
Venue: arXiv cs.DS 2018
Year: 2018

Finding locally optimal solutions for max-cut and max-$k$-cut are well-known PLS-complete problems. An instinctive approach to finding such a locally optimum solution is the FLIP method. Even though FLIP requires exponential time in worst-case instances, it tends to terminate quickly in practical instances. To explain this discrepancy, the run-time of FLIP has been studied in the smoothed complexity framework. Etscheid and Röglin showed that the smoothed complexity of FLIP for max-cut in arbitrary graphs is quasi-polynomial. Angel, Bubeck, Peres, and Wei showed that the smoothed complexity of FLIP for max-cut in complete graphs is $O(ϕ^5n^{15.1})$, where $ϕ$ is an upper bound on the random edge-weight density and $n$ is the number of vertices in the input graph. While Angel et al.'s result showed the first polynomial smoothed complexity, they also conjectured that their run-time bound is far from optimal. In this work, we make substantial progress towards improving the run-time bound. We prove that the smoothed complexity of FLIP in complete graphs is $O(ϕn^{7.83})$. Our results are based on a carefully chosen matrix whose rank captures the run-time of the method along with improved rank bounds for this matrix and an improved union bound based on this matrix. In addition, our techniques provide a general framework for analyzing FLIP in the smoothed framework. We illustrate this general framework by showing that the smoothed complexity of FLIP for max-$3$-cut in complete graphs is polynomial and for max-$k$-cut in arbitrary graphs is quasi-polynomial. We believe that our techniques should also be of interest towards addressing the smoothed complexity of FLIP for max-$k$-cut in complete graphs for larger constants $k$.
Private Data Objects: an Overview
Venue: arXiv cs.CR 2018
Year: 2018

We present Private Data Objects (PDOs), a technology that enables mutually untrusted parties to run smart contracts over private data. PDOs result from the integration of a distributed ledger and Intel Secure Guard Extensions (SGX). In particular, contracts run off-ledger in secure enclaves using Intel SGX, which preserves data confidentiality, execution integrity and enforces data access policies (as opposed to raw data access). A distributed ledger verifies and records transactions produced by PDOs, in order to provide a single authoritative instance of such objects. This allows contracting parties to retrieve and check data related to contract and enclave instances, as well as to serialize and commit contract state updates. The design and the development of PDOs is an ongoing research effort, and open source code is available and hosted by Hyperledger Labs [5, 7].
Task Replication for Vehicular Edge Computing: A Combinatorial Multi-Armed Bandit based Approach
Venue: arXiv cs.IT 2018
Year: 2018

In vehicular edge computing (VEC) system, some vehicles with surplus computing resources can provide computation task offloading opportunities for other vehicles or pedestrians. However, vehicular network is highly dynamic, with fast varying channel states and computation loads. These dynamics are difficult to model or to predict, but they have major impact on the quality of service (QoS) of task offloading, including delay performance and service reliability. Meanwhile, the computing resources in VEC are often redundant due to the high density of vehicles. To improve the QoS of VEC and exploit the abundant computing resources on vehicles, we propose a learning-based task replication algorithm (LTRA) based on combinatorial multi-armed bandit (CMAB) theory, in order to minimize the average offloading delay. LTRA enables multiple vehicles to process the replicas of the same task simultaneously, and vehicles that require computing services can learn the delay performance of other vehicles while offloading tasks. We take the occurrence time of vehicles into consideration, and redesign the utility function of existing CMAB algorithm, so that LTRA can adapt to the time varying network topology of VEC. We use a realistic highway scenario to evaluate the delay performance and service reliability of LTRA through simulations, and show that compared with single task offloading, LTRA can improve the task completion ratio with deadline 0.6s from 80% to 98%.
Governing autonomous vehicles: emerging responses for safety, liability, privacy, cybersecurity, and industry risks
Venue: arXiv cs.CY 2018
Year: 2018

The benefits of autonomous vehicles (AVs) are widely acknowledged, but there are concerns about the extent of these benefits and AV risks and unintended consequences. In this article, we first examine AVs and different categories of the technological risks associated with them. We then explore strategies that can be adopted to address these risks, and explore emerging responses by governments for addressing AV risks. Our analyses reveal that, thus far, governments have in most instances avoided stringent measures in order to promote AV developments and the majority of responses are non-binding and focus on creating councils or working groups to better explore AV implications. The US has been active in introducing legislations to address issues related to privacy and cybersecurity. The UK and Germany, in particular, have enacted laws to address liability issues, other countries mostly acknowledge these issues, but have yet to implement specific strategies. To address privacy and cybersecurity risks strategies ranging from introduction or amendment of non-AV specific legislation to creating working groups have been adopted. Much less attention has been paid to issues such as environmental and employment risks, although a few governments have begun programmes to retrain workers who might be negatively affected.
BRIEF: Backward Reduction of CNNs with Information Flow Analysis
Venue: arXiv cs.LG 2018
Year: 2018

This paper proposes BRIEF, a backward reduction algorithm that explores compact CNN-model designs from the information flow perspective. This algorithm can remove substantial non-zero weighting parameters (redundant neural channels) of a network by considering its dynamic behavior, which traditional model-compaction techniques cannot achieve. With the aid of our proposed algorithm, we achieve significant model reduction on ResNet-34 in the ImageNet scale (32.3% reduction), which is 3X better than the previous result (10.8%). Even for highly optimized models such as SqueezeNet and MobileNet, we can achieve additional 10.81% and 37.56% reduction, respectively, with negligible performance degradation.
Enhancing Middleware-based IoT Applications through Run-Time Pluggable QoS Management Mechanisms. Application to a oneM2M compliant IoT Middleware
Venue: arXiv cs.DC 2018
Year: 2018

In the recent years, telecom and computer networks have witnessed new concepts and technologies through Network Function Virtualization (NFV) and Software-Defined Networking (SDN). SDN, which allows applications to have a control over the network, and NFV, which allows deploying network functions in virtualized environments, are two paradigms that are increasingly used for the Internet of Things (IoT). This Internet (IoT) brings the promise to interconnect billions of devices in the next few years rises several scientific challenges in particular those of the satisfaction of the quality of service (QoS) required by the IoT applications. In order to address this problem, we have identified two bottlenecks with respect to the QoS: the traversed networks and the intermediate entities that allows the application to interact with the IoT devices. In this paper, we first present an innovative vision of a "network function" with respect to their deployment and runtime environment. Then, we describe our general approach of a solution that consists in the dynamic, autonomous, and seamless deployment of QoS management mechanisms. We also describe the requirements for the implementation of such approach. Finally, we present a redirection mechanism, implemented as a network function, allowing the seamless control of the data path of a given middleware traffic. This mechanism is assessed through a use case related to vehicular transportation.
An Adjustable Heat Conduction based KNN Approach for Session-based Recommendation
Venue: arXiv cs.IR 2018
Year: 2018

The KNN approach, which is widely used in recommender systems because of its efficiency, robustness and interpretability, is proposed for session-based recommendation recently and outperforms recurrent neural network models. It captures the most recent co-occurrence information of items by considering the interaction time. However, it neglects the co-occurrence information of items in the historical behavior which is interacted earlier and cannot discriminate the impact of items and sessions with different popularity. Due to these observations, this paper presents a new contextual KNN approach to address these issues for session-based recommendation. Specifically, a diffusion-based similarity method is proposed for considering the popularity of vertices in session-item bipartite network, and a candidate selection method is proposed to capture the items that are co-occurred with different historical clicked items in the same session efficiently. Comprehensive experiments are conducted to demonstrate the effectiveness of our KNN approach over the state-of-the-art KNN approach for session-based recommendation on three benchmark datasets.
Reviewing, indicating, and counting books for modern research evaluation systems
Venue: arXiv cs.DL 2018
Year: 2018

In this chapter, we focus on the specialists who have helped to improve the conditions for book assessments in research evaluation exercises, with empirically based data and insights supporting their greater integration. Our review highlights the research carried out by four types of expert communities, referred to as the monitors, the subject classifiers, the indexers and the indicator constructionists. Many challenges lie ahead for scholars affiliated with these communities, particularly the latter three. By acknowledging their unique, yet interrelated roles, we show where the greatest potential is for both quantitative and qualitative indicator advancements in book-inclusive evaluation systems.
Automatic acoustic detection of birds through deep learning: the first Bird Audio Detection challenge
Venue: arXiv cs.SD 2018
Year: 2018

Assessing the presence and abundance of birds is important for monitoring specific species as well as overall ecosystem health. Many birds are most readily detected by their sounds, and thus passive acoustic monitoring is highly appropriate. Yet acoustic monitoring is often held back by practical limitations such as the need for manual configuration, reliance on example sound libraries, low accuracy, low robustness, and limited ability to generalise to novel acoustic conditions. Here we report outcomes from a collaborative data challenge showing that with modern machine learning including deep learning, general-purpose acoustic bird detection can achieve very high retrieval rates in remote monitoring data --- with no manual recalibration, and no pre-training of the detector for the target species or the acoustic conditions in the target environment. Multiple methods were able to attain performance of around 88% AUC (area under the ROC curve), much higher performance than previous general-purpose methods. We present new acoustic monitoring datasets, summarise the machine learning techniques proposed by challenge teams, conduct detailed performance evaluation, and discuss how such approaches to detection can be integrated into remote monitoring projects.
Multi-agents features on Android platforms
Venue: arXiv cs.MA 2018
Year: 2018

The current paper shows the multi-agents capabilities to make a valid and flexible application when using a framework. Agent-based functions were used within JADE framework to make an Android messenger application with all requirements included. In the paper are described the architecture, the main functions and the databases integration of the user friendly agent-based application. There are included existing and possible multi-agents characteristics to provide integration with mobile platforms and storage challenges to improve the user experience through data mining.
Assessing fish abundance from underwater video using deep neural networks
Venue: arXiv cs.CV 2018
Year: 2018

Uses of underwater videos to assess diversity and abundance of fish are being rapidly adopted by marine biologists. Manual processing of videos for quantification by human analysts is time and labour intensive. Automatic processing of videos can be employed to achieve the objectives in a cost and time-efficient way. The aim is to build an accurate and reliable fish detection and recognition system, which is important for an autonomous robotic platform. However, there are many challenges involved in this task (e.g. complex background, deformation, low resolution and light propagation). Recent advancement in the deep neural network has led to the development of object detection and recognition in real time scenarios. An end-to-end deep learning-based architecture is introduced which outperformed the state of the art methods and first of its kind on fish assessment task. A Region Proposal Network (RPN) introduced by an object detector termed as Faster R-CNN was combined with three classification networks for detection and recognition of fish species obtained from Remote Underwater Video Stations (RUVS). An accuracy of 82.4% (mAP) obtained from the experiments are much higher than previously proposed methods.
Efficient DSP and Circuit Architectures for Massive MIMO: State-of-the-Art and Future Directions
Venue: arXiv cs.IT 2018
Year: 2018

Massive MIMO is a compelling wireless access concept that relies on the use of an excess number of base-station antennas, relative to the number of active terminals. This technology is a main component of 5G New Radio (NR) and addresses all important requirements of future wireless standards: a great capacity increase, the support of many simultaneous users, and improvement in energy efficiency. Massive MIMO requires the simultaneous processing of signals from many antenna chains, and computational operations on large matrices. The complexity of the digital processing has been viewed as a fundamental obstacle to the feasibility of Massive MIMO in the past. Recent advances on system-algorithm-hardware co-design have led to extremely energy-efficient implementations. These exploit opportunities in deeply-scaled silicon technologies and perform partly distributed processing to cope with the bottlenecks encountered in the interconnection of many signals. For example, prototype ASIC implementations have demonstrated zero-forcing precoding in real time at a 55 mW power consumption (20 MHz bandwidth, 128 antennas, multiplexing of 8 terminals). Coarse and even error-prone digital processing in the antenna paths permits a reduction of consumption with a factor of 2 to 5. This article summarizes the fundamental technical contributions to efficient digital signal processing for Massive MIMO. The opportunities and constraints on operating on low-complexity RF and analog hardware chains are clarified. It illustrates how terminals can benefit from improved energy efficiency. The status of technology and real-life prototypes discussed. Open challenges and directions for future research are suggested.